





'''simple docstring'''






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCAmelCase									=   {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def   __A    (				a_    :	List[Any]						):
		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts						)
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config						)
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights						)

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def   __A    (				a_    :	Dict   ,a_    :	List[Any]						):
		if args.student_type == "roberta":
				lowerCAmelCase  :     Tuple       						= False
		elif args.student_type == "gpt2":
				lowerCAmelCase  :     Tuple       						= False






def   __A    (				a_    :	Union[str, Any]   ,a_    :	Optional[Any]						):
		if args.student_type == "roberta":
				lowerCAmelCase  :     List[str]       						= False






def   __A    (				):
		lowerCAmelCase  :     Tuple       						= argparse.ArgumentParser(description="Training"						)
		parser.add_argument("--force"   ,action="store_true"   ,help="Overwrite dump_path if it already exists."						)

		parser.add_argument(
		    "--dump_path"   ,type=a_   ,required=a_   ,help="The output directory (log, checkpoints, parameters, etc.)"						)
		parser.add_argument(
		    "--data_file"   ,type=a_   ,required=a_   ,help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."   ,)

		parser.add_argument(
		    "--student_type"   ,type=a_   ,choices=["distilbert", "roberta", "gpt2"]   ,required=a_   ,help="The student type (DistilBERT, RoBERTa)."   ,)
		parser.add_argument("--student_config"   ,type=a_   ,required=a_   ,help="Path to the student configuration."						)
		parser.add_argument(
		    "--student_pretrained_weights"   ,default=a_   ,type=a_   ,help="Load student initialization checkpoint."						)

		parser.add_argument(
		    "--teacher_type"   ,choices=["bert", "roberta", "gpt2"]   ,required=a_   ,help="Teacher type (BERT, RoBERTa)."						)
		parser.add_argument("--teacher_name"   ,type=a_   ,required=a_   ,help="The teacher model."						)

		parser.add_argument("--temperature"   ,default=2.0   ,type=a_   ,help="Temperature for the softmax temperature."						)
		parser.add_argument(
		    "--alpha_ce"   ,default=0.5   ,type=a_   ,help="Linear weight for the distillation loss. Must be >=0."						)
		parser.add_argument(
		    "--alpha_mlm"   ,default=0.0   ,type=a_   ,help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."   ,)
		parser.add_argument("--alpha_clm"   ,default=0.5   ,type=a_   ,help="Linear weight for the CLM loss. Must be >=0."						)
		parser.add_argument("--alpha_mse"   ,default=0.0   ,type=a_   ,help="Linear weight of the MSE loss. Must be >=0."						)
		parser.add_argument(
		    "--alpha_cos"   ,default=0.0   ,type=a_   ,help="Linear weight of the cosine embedding loss. Must be >=0."						)

		parser.add_argument(
		    "--mlm"   ,action="store_true"   ,help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."						)
		parser.add_argument(
		    "--mlm_mask_prop"   ,default=0.1_5   ,type=a_   ,help="Proportion of tokens for which we need to make a prediction."   ,)
		parser.add_argument("--word_mask"   ,default=0.8   ,type=a_   ,help="Proportion of tokens to mask out."						)
		parser.add_argument("--word_keep"   ,default=0.1   ,type=a_   ,help="Proportion of tokens to keep."						)
		parser.add_argument("--word_rand"   ,default=0.1   ,type=a_   ,help="Proportion of tokens to randomly replace."						)
		parser.add_argument(
		    "--mlm_smoothing"   ,default=0.7   ,type=a_   ,help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."   ,)
		parser.add_argument("--token_counts"   ,type=a_   ,help="The token counts in the data_file for MLM."						)

		parser.add_argument(
		    "--restrict_ce_to_mask"   ,action="store_true"   ,help="If true, compute the distillation loss only the [MLM] prediction distribution."   ,)
		parser.add_argument(
		    "--freeze_pos_embs"   ,action="store_true"   ,help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."   ,)
		parser.add_argument(
		    "--freeze_token_type_embds"   ,action="store_true"   ,help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."   ,)

		parser.add_argument("--n_epoch"   ,type=a_   ,default=3   ,help="Number of pass on the whole dataset."						)
		parser.add_argument("--batch_size"   ,type=a_   ,default=5   ,help="Batch size (for each process)."						)
		parser.add_argument(
		    "--group_by_size"   ,action="store_false"   ,help="If true, group sequences that have similar length into the same batch. Default is true."   ,)

		parser.add_argument(
		    "--gradient_accumulation_steps"   ,type=a_   ,default=5_0   ,help="Gradient accumulation for larger training batches."   ,)
		parser.add_argument("--warmup_prop"   ,default=0.0_5   ,type=a_   ,help="Linear warmup proportion."						)
		parser.add_argument("--weight_decay"   ,default=0.0   ,type=a_   ,help="Weight decay if we apply some."						)
		parser.add_argument("--learning_rate"   ,default=5e-4   ,type=a_   ,help="The initial learning rate for Adam."						)
		parser.add_argument("--adam_epsilon"   ,default=1e-6   ,type=a_   ,help="Epsilon for Adam optimizer."						)
		parser.add_argument("--max_grad_norm"   ,default=5.0   ,type=a_   ,help="Max gradient norm."						)
		parser.add_argument("--initializer_range"   ,default=0.0_2   ,type=a_   ,help="Random initialization range."						)

		parser.add_argument(
		    "--fp16"   ,action="store_true"   ,help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,)
		parser.add_argument(
		    "--fp16_opt_level"   ,type=a_   ,default="O1"   ,help=(
		        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
		        "See details at https://nvidia.github.io/apex/amp.html"
		    )   ,)
		parser.add_argument("--n_gpu"   ,type=a_   ,default=1   ,help="Number of GPUs in the node."						)
		parser.add_argument("--local_rank"   ,type=a_   ,default=-1   ,help="Distributed training - Local rank"						)
		parser.add_argument("--seed"   ,type=a_   ,default=5_6   ,help="Random seed"						)

		parser.add_argument("--log_interval"   ,type=a_   ,default=5_0_0   ,help="Tensorboard logging interval."						)
		parser.add_argument("--checkpoint_interval"   ,type=a_   ,default=4_0_0_0   ,help="Checkpoint interval."						)
		lowerCAmelCase  :     Tuple       						= parser.parse_args()
		sanity_checks(a_						)

		# ARGS #
		init_gpu_params(a_						)
		set_seed(a_						)
		if args.is_master:
				if os.path.exists(args.dump_path						):
						if not args.force:
								raise ValueError(
								    f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
								    " itUse `--force` if you want to overwrite it"						)
						else:
								shutil.rmtree(args.dump_path						)

				if not os.path.exists(args.dump_path						):
						os.makedirs(args.dump_path						)
				logger.info(f'''Experiment will be dumped and logged in {args.dump_path}'''						)

				# SAVE PARAMS #
				logger.info(f'''Param: {args}'''						)
				with open(os.path.join(args.dump_path   ,"parameters.json"						)   ,"w"						) as f:
						json.dump(vars(a_						)   ,a_   ,indent=4						)
				git_log(args.dump_path						)

		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= MODEL_CLASSES[args.student_type]
		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		lowerCAmelCase  :     Optional[Any]       						= teacher_tokenizer_class.from_pretrained(args.teacher_name						)
		lowerCAmelCase  :     List[str]       						= {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				lowerCAmelCase  :     Tuple       						= tokenizer.all_special_tokens.index(a_						)
				lowerCAmelCase  :     List[str]       						= tokenizer.all_special_ids[idx]
		logger.info(f'''Special tokens {special_tok_ids}'''						)
		lowerCAmelCase  :     List[Any]       						= special_tok_ids
		lowerCAmelCase  :     str       						= tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(f'''Loading data from {args.data_file}'''						)
		with open(args.data_file   ,"rb"						) as fp:
				lowerCAmelCase  :     Any       						= pickle.load(a_						)

		if args.mlm:
				logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''						)
				with open(args.token_counts   ,"rb"						) as fp:
						lowerCAmelCase  :     List[Any]       						= pickle.load(a_						)

				lowerCAmelCase  :     int       						= np.maximum(a_   ,1						) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						lowerCAmelCase  :     List[Any]       						= 0.0  # do not predict special tokens
				lowerCAmelCase  :     Tuple       						= torch.from_numpy(a_						)
		else:
				lowerCAmelCase  :     Dict       						= None

		lowerCAmelCase  :     int       						= LmSeqsDataset(params=a_   ,data=a_						)
		logger.info("Data loader created."						)

		# STUDENT #
		logger.info(f'''Loading student config from {args.student_config}'''						)
		lowerCAmelCase  :     Union[str, Any]       						= student_config_class.from_pretrained(args.student_config						)
		lowerCAmelCase  :     List[str]       						= True

		if args.student_pretrained_weights is not None:
				logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
				lowerCAmelCase  :     Optional[Any]       						= student_model_class.from_pretrained(args.student_pretrained_weights   ,config=a_						)
		else:
				lowerCAmelCase  :     int       						= student_model_class(a_						)

		if args.n_gpu > 0:
				student.to(f'''cuda:{args.local_rank}'''						)
		logger.info("Student loaded."						)

		# TEACHER #
		lowerCAmelCase  :     Optional[Any]       						= teacher_model_class.from_pretrained(args.teacher_name   ,output_hidden_states=a_						)
		if args.n_gpu > 0:
				teacher.to(f'''cuda:{args.local_rank}'''						)
		logger.info(f'''Teacher loaded from {args.teacher_name}.'''						)

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(a_   ,a_						)
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(a_   ,a_						)

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0						) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		lowerCAmelCase  :     Tuple       						= Distiller(
		    params=a_   ,dataset=a_   ,token_probs=a_   ,student=a_   ,teacher=a_						)
		distiller.train()
		logger.info("Let's go get some drinks."						)


if __name__ == "__main__":
					main()










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



